{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3WDXGO4J6PYWVEUZQDRHVKGI5C","short_pith_number":"pith:3WDXGO4J","schema_version":"1.0","canonical_sha256":"dd87733b89f3f16a929980e27aa8c8e89127f1587aabf1cba4920eca9af97c2c","source":{"kind":"arxiv","id":"2310.05845","version":1},"attestation_state":"computed","paper":{"title":"GraphLLM: Boosting Graph Reasoning Ability of Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kaiqiao Han, Liang Wu, Tianjie Zhang, Xiaohai Hu, Xuanwen Huang, Yang Yang, Ziwei Chai","submitted_at":"2023-10-09T16:42:00Z","abstract_excerpt":"The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of conve"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.05845","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-09T16:42:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"77b0d129ce439dfb786e446697f53077f2c1fdb2cedecf82fc571c99978a40da","abstract_canon_sha256":"adb0cecd9653035d9ffa2ad0329c32b02445f1c3f4a81bb6c1febced49a35f1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:58:50.582545Z","signature_b64":"Tyg5B9VWCkV6co2VMnTWHEB+4+07ahl0gSrjrHjBKIobzeIhmhEvHbY2gZoqU8h7Bcy/bu0+62Y2vgzYBsZQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd87733b89f3f16a929980e27aa8c8e89127f1587aabf1cba4920eca9af97c2c","last_reissued_at":"2026-07-05T06:58:50.582132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:58:50.582132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraphLLM: Boosting Graph Reasoning Ability of Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kaiqiao Han, Liang Wu, Tianjie Zhang, Xiaohai Hu, Xuanwen Huang, Yang Yang, Ziwei Chai","submitted_at":"2023-10-09T16:42:00Z","abstract_excerpt":"The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of conve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.05845","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.05845/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.05845","created_at":"2026-07-05T06:58:50.582192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.05845v1","created_at":"2026-07-05T06:58:50.582192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.05845","created_at":"2026-07-05T06:58:50.582192+00:00"},{"alias_kind":"pith_short_12","alias_value":"3WDXGO4J6PYW","created_at":"2026-07-05T06:58:50.582192+00:00"},{"alias_kind":"pith_short_16","alias_value":"3WDXGO4J6PYWVEUZ","created_at":"2026-07-05T06:58:50.582192+00:00"},{"alias_kind":"pith_short_8","alias_value":"3WDXGO4J","created_at":"2026-07-05T06:58:50.582192+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29773","citing_title":"GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2501.17549","citing_title":"Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2501.00309","citing_title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06671","citing_title":"GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C","json":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C.json","graph_json":"https://pith.science/api/pith-number/3WDXGO4J6PYWVEUZQDRHVKGI5C/graph.json","events_json":"https://pith.science/api/pith-number/3WDXGO4J6PYWVEUZQDRHVKGI5C/events.json","paper":"https://pith.science/paper/3WDXGO4J"},"agent_actions":{"view_html":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C","download_json":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C.json","view_paper":"https://pith.science/paper/3WDXGO4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.05845&json=true","fetch_graph":"https://pith.science/api/pith-number/3WDXGO4J6PYWVEUZQDRHVKGI5C/graph.json","fetch_events":"https://pith.science/api/pith-number/3WDXGO4J6PYWVEUZQDRHVKGI5C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C/action/storage_attestation","attest_author":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C/action/author_attestation","sign_citation":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C/action/citation_signature","submit_replication":"https://pith.science/pith/3WDXGO4J6PYWVEUZQDRHVKGI5C/action/replication_record"}},"created_at":"2026-07-05T06:58:50.582192+00:00","updated_at":"2026-07-05T06:58:50.582192+00:00"}